{
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   "source": [
    "# Graphical Pipelines\n",
    "\n",
    "### Content of this Notebook:\n",
    "* Understanding what are graphical pipelines\n",
    "* Understanding the API of graphical pipelines\n",
    "* Examples of simple pipelines and how they can be implemented with graphical pipelines.\n",
    "* More complex graphical pipeline (Forecasting + GridSearch)\n",
    "* Grid search with a graphical pipeline\n",
    "\n",
    "\n",
    "\n",
    "### What are Graphical Pipelines?\n",
    "Recap sequential pipelines:\n",
    "\n",
    "<img src=\"img/sequential_pipeline.png\" width=750 />\n",
    "\n",
    "Many tasks are non-sequential. To solve this two possibilities exist:\n",
    "1. Nesting Sequential Pipelines.\n",
    "2. Using Graphical Pipelines.\n",
    "\n",
    "\n",
    "Thus, there is the generalised graphial pipeline.\n",
    "* Graphical means that different steps may share the same predecessor or provide their outputs to the same successor (the dataflows can branch and merge).\n",
    "<img src=\"img/graphical_pipeline.png\" width=750 />\n",
    "\n",
    "\n",
    "* Generalised means that the pipeline can be used for multiple tasks (e.g. forecasting, classification, ...).\n",
    "\n",
    "**Note**\n",
    "\n",
    "The graphical pipeline is a new feature, Thus, if you are considering any issues, we would be happy to get feedback on the graphical pipeline.\n",
    "\n",
    "\n",
    "### Potential Use-Cases\n",
    "There exist various potential use-case for the graphical pipeline. In the following, we focus on a forecasting and a classification pipeline.\n",
    "#### Forecasting Use-Case for Graphical Pipelines\n",
    "\n",
    "\n",
    "The input of forecasters depends on the output of other forecasters, which same the same input.\n",
    "* Forecaster could use the same preprocessing (branching of data flow)\n",
    "* Forecaster could use outputs of multiple predeccessors (merging of data flow)\n",
    "\n",
    "<img src=\"img/graphical_pipeline_example.png\" width=900 />\n",
    "\n",
    "\n",
    "**Note:** The current experimental state of the graphical pipeline does not fully support this use-case. However, we are working on this. If you are interested in this use-case and want to contribute, please contact us.\n",
    "\n",
    "### Credits\n",
    "The graphical pipeline was first developed by pyWATTS [1] and was then adapted for sktime. The original implementation can be found [pyWATTS](https://github.com/KIT-IAI/pyWATTS). pyWATTS is a open source library developed at the Institute of Applied Informatics and Automation at the KIT and funded by HelmholtzAI.\n",
    "\n",
    "> [1] Heidrich, Benedikt, et al. \"pyWATTS: Python workflow automation tool for time series.\" arXiv preprint arXiv:2106.10157 (2021)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    "**Note:** The current experimental state of the graphical pipeline does not fully support this use-case. However, we are working on this. If you are interested in this use-case and want to contribute, please contact us.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## How to build a Graphical Pipeline\n",
    "\n",
    "Let us first visualise a simple forecasting pipeline, we want to construct: \n",
    "\n",
    "\n",
    "<img src=\"img/forecasting_pipeline.png\" width=750 />\n",
    "\n",
    "\n",
    "Then we are having to ways on how to construct this pipeline with the graphical pipeline\n",
    "\n",
    "1. Pass all steps to the pipeline during initialisation as for the sequential pipelines.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sktime.forecasting.sarimax import SARIMAX\n",
    "from sktime.pipeline.pipeline import Pipeline\n",
    "from sktime.transformations.series.difference import Differencer\n",
    "\n",
    "differencer = Differencer()\n",
    "\n",
    "general_pipeline = Pipeline(\n",
    "    [\n",
    "        {\"skobject\": differencer, \"name\": \"differencer\", \"edges\": {\"X\": \"y\"}},\n",
    "        {\n",
    "            \"skobject\": SARIMAX(),\n",
    "            \"name\": \"sarimax\",\n",
    "            \"edges\": {\"X\": \"X\", \"y\": \"differencer\"},\n",
    "        },\n",
    "        {\n",
    "            \"skobject\": differencer,\n",
    "            \"name\": \"differencer_inv\",\n",
    "            \"edges\": {\"X\": \"sarimax\"},\n",
    "            \"method\": \"inverse_transform\",\n",
    "        },\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Create a pipeline object and add the steps one by one.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "general_pipeline = Pipeline()\n",
    "differencer = Differencer()\n",
    "\n",
    "general_pipeline = general_pipeline.add_step(\n",
    "    differencer, \"differencer\", edges={\"X\": \"y\"}\n",
    ")\n",
    "general_pipeline = general_pipeline.add_step(\n",
    "    SARIMAX(), \"sarimax\", edges={\"X\": \"X\", \"y\": \"differencer\"}\n",
    ")\n",
    "general_pipeline = general_pipeline.add_step(\n",
    "    differencer, \"differencer_inv\", edges={\"X\": \"sarimax\"}, method=\"inverse_transform\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Explanation of the parameters\n",
    "\n",
    "The `add_step`'s parameter or key of the dicts in the step list during initialisation are:\n",
    "\n",
    "* skobject: The sktime object added to the pipeline\n",
    "* name: The name of the step\n",
    "* edges: The keys of the dictionary indicate the input of the skobject (X or y), and the values are the names of the steps that should be connected to the input argument. Note subsetting using `__` and feature union via lists are supported.\n",
    "* method: The skobject's method that should be called. If not provided, the default method would be inferred based on the added skobject. This parameter is used for the inverse_transform method. Optional.\n",
    "* kwargs: Additional keyword arguments passed to the sktime object. Optional.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us fit the pipeline and make a prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1959    67213.735362\n",
       "1960    68328.076310\n",
       "1961    68737.861398\n",
       "1962    71322.894026\n",
       "Freq: A-DEC, Name: TOTEMP, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.datasets import load_longley\n",
    "from sktime.forecasting.model_selection import temporal_train_test_split\n",
    "\n",
    "y, X = load_longley()\n",
    "y_train, y_test, X_train, X_test = temporal_train_test_split(y, X)\n",
    "\n",
    "general_pipeline.fit(y=y_train, X=X_train, fh=[1, 2, 3, 4])\n",
    "general_pipeline.predict(X=X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Further Examples\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Classification Pipeline\n",
    "A simple classification pipeline implemented using the graphical pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier\n",
    "from sktime.transformations.series.exponent import ExponentTransformer\n",
    "\n",
    "general_pipeline = Pipeline()\n",
    "general_pipeline = general_pipeline.add_step(\n",
    "    ExponentTransformer(), \"exponent\", edges={\"X\": \"X\"}\n",
    ")\n",
    "general_pipeline = general_pipeline.add_step(\n",
    "    KNeighborsTimeSeriesClassifier(), \"classifier\", edges={\"X\": \"exponent\", \"y\": \"y\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or alternatively defined using the constructor API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "general_pipeline = Pipeline(\n",
    "    [\n",
    "        {\"skobject\": ExponentTransformer(), \"name\": \"exponent\", \"edges\": {\"X\": \"X\"}},\n",
    "        {\n",
    "            \"skobject\": KNeighborsTimeSeriesClassifier(),\n",
    "            \"name\": \"classifier\",\n",
    "            \"edges\": {\"X\": \"exponent\", \"y\": \"y\"},\n",
    "        },\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "This pipeline can be visualised as follows:\n",
    "\n",
    "<img src=\"img/classification_pipeline.png\" width=750 />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1', '2', '0', '1', '2', '0', '1', '2', '0', '1', '2', '0',\n",
       "       '1', '2', '0', '1', '2', '0', '1', '2', '0', '1', '2', '0', '1',\n",
       "       '2', '0', '1', '2', '0', '1', '2', '0', '1', '2'], dtype='<U1')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.datasets import load_arrow_head\n",
    "\n",
    "X, y = load_arrow_head(split=\"train\", return_X_y=True)\n",
    "general_pipeline.fit(X=X, y=y)\n",
    "general_pipeline.predict(X=X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## A More Complex Example\n",
    "\n",
    "The considered use-case is to forecast the inflation using forecasts of the real gross domestic product, real disposable personal income, and the unemployment rate. Furthermore the unemployment rate is forecasted using the same features except the unemployment rate itself.\n",
    "\n",
    "<img src=\"img/graphical_pipeline_example.png\" width=750 />\n",
    "\n",
    "\n",
    "The data is taken from the macrodata dataset from the statsmodels package.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**Note** We stick with the add_step in the following.\n",
    "\n",
    "Create Graphical Pipeline Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-34336eb9-3246-4dd2-82c0-cfa130157115 div.sk-text-repr-fallback {display: none;}</style><div id='sk-34336eb9-3246-4dd2-82c0-cfa130157115' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('24549e2e-b542-4ee4-a77e-4ca2eceafc7f') type=\"checkbox\" checked><label for=UUID('24549e2e-b542-4ee4-a77e-4ca2eceafc7f') class='sk-toggleable__label sk-toggleable__label-arrow'>Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline()\n",
    "pipe.set_config(warnings=\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add Preprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sktime.transformations.series.adapt import TabularToSeriesAdaptor\n",
    "from sktime.transformations.series.detrend import Deseasonalizer\n",
    "\n",
    "pipe = pipe.add_step(\n",
    "    TabularToSeriesAdaptor(StandardScaler()),\n",
    "    name=\"scaler\",\n",
    "    edges={\"X\": \"X__realgdp_realdpi_unemp\"},\n",
    ")\n",
    "pipe = pipe.add_step(\n",
    "    Deseasonalizer(sp=4), name=\"deseasonalizer\", edges={\"X\": \"X__realgdp_realdpi\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add forecastesr for GDP and DPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso, Ridge\n",
    "\n",
    "from sktime.forecasting.compose import make_reduction\n",
    "\n",
    "pipe = pipe.add_step(\n",
    "    make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "    name=\"forecaster_gdp\",\n",
    "    edges={\"y\": \"deseasonalizer__realgdp\"},\n",
    ")\n",
    "\n",
    "pipe = pipe.add_step(\n",
    "    make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "    name=\"forecaster_dpi\",\n",
    "    edges={\"y\": \"deseasonalizer__realdpi\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add Forecaster for unemployment rate that depends on forecasts of GDP and DPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = pipe.add_step(\n",
    "    make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "    name=\"forecaster_unemp\",\n",
    "    edges={\n",
    "        \"y\": \"scaler__unemp\",\n",
    "        \"X\": [\n",
    "            \"forecaster_gdp\",\n",
    "            \"forecaster_dpi\",\n",
    "        ],\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add forecaster for the inflation that depends on forecasted DPI and unemployment rate\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = pipe.add_step(\n",
    "    make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "    name=\"forecaster_inflation\",\n",
    "    edges={\"X\": [\"forecaster_dpi\", \"forecaster_unemp\"], \"y\": \"y\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data and split them into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959Q1</th>\n",
       "      <td>2710.349</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959Q2</th>\n",
       "      <td>2778.801</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959Q3</th>\n",
       "      <td>2775.488</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959Q4</th>\n",
       "      <td>2785.204</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960Q1</th>\n",
       "      <td>2847.699</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005Q3</th>\n",
       "      <td>12683.153</td>\n",
       "      <td>9308.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005Q4</th>\n",
       "      <td>12748.699</td>\n",
       "      <td>9358.7</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006Q1</th>\n",
       "      <td>12915.938</td>\n",
       "      <td>9533.8</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006Q2</th>\n",
       "      <td>12962.462</td>\n",
       "      <td>9617.3</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006Q3</th>\n",
       "      <td>12965.916</td>\n",
       "      <td>9662.5</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          realgdp  realdpi  unemp\n",
       "Period                           \n",
       "1959Q1   2710.349   1886.9    5.8\n",
       "1959Q2   2778.801   1919.7    5.1\n",
       "1959Q3   2775.488   1916.4    5.3\n",
       "1959Q4   2785.204   1931.3    5.6\n",
       "1960Q1   2847.699   1955.5    5.2\n",
       "...           ...      ...    ...\n",
       "2005Q3  12683.153   9308.0    5.0\n",
       "2005Q4  12748.699   9358.7    4.9\n",
       "2006Q1  12915.938   9533.8    4.7\n",
       "2006Q2  12962.462   9617.3    4.7\n",
       "2006Q3  12965.916   9662.5    4.7\n",
       "\n",
       "[191 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.datasets import load_macroeconomic\n",
    "from sktime.forecasting.base import ForecastingHorizon\n",
    "\n",
    "data = load_macroeconomic()\n",
    "\n",
    "X = data[[\"realgdp\", \"realdpi\", \"unemp\"]]\n",
    "y = data[[\"infl\"]]\n",
    "fh = ForecastingHorizon([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
    "\n",
    "y_train, y_test, X_train, X_test = temporal_train_test_split(y, X=X, fh=fh)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>infl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006Q4</th>\n",
       "      <td>3.090428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q1</th>\n",
       "      <td>1.676421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q2</th>\n",
       "      <td>0.219586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q3</th>\n",
       "      <td>1.570087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q4</th>\n",
       "      <td>0.350137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q1</th>\n",
       "      <td>0.438966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q2</th>\n",
       "      <td>0.615457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q3</th>\n",
       "      <td>0.119022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q4</th>\n",
       "      <td>0.257887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009Q1</th>\n",
       "      <td>0.129785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009Q2</th>\n",
       "      <td>-0.056094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009Q3</th>\n",
       "      <td>-0.066123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            infl\n",
       "Period          \n",
       "2006Q4  3.090428\n",
       "2007Q1  1.676421\n",
       "2007Q2  0.219586\n",
       "2007Q3  1.570087\n",
       "2007Q4  0.350137\n",
       "2008Q1  0.438966\n",
       "2008Q2  0.615457\n",
       "2008Q3  0.119022\n",
       "2008Q4  0.257887\n",
       "2009Q1  0.129785\n",
       "2009Q2 -0.056094\n",
       "2009Q3 -0.066123"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(y=y_train, X=X_train, fh=fh)\n",
    "result = pipe.predict(X=None, fh=y_test.index)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "infl    20.103326\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((result - y_test) ** 2).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### Grid Search with graphical pipeline\n",
    "\n",
    "This pipeline has multiple parameters that might be tested to find the configurations. These parameters include:\n",
    "* which forecaster should be used for which variable -> `MultiplexForecaster`\n",
    "* what should be the hyperparameters of the forecaster\n",
    "* which features should be used for the different forecasters -> Tune the edges of the graphical pipeline!\n",
    "\n",
    "<img src=\"img/graphical_pipeline_example_grid.png\" width=900 />\n",
    "\n",
    "Since we do forecasting, we use the ForecastingGridSearchCV."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Create blue print of the pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sktime.forecasting.compose import MultiplexForecaster\n",
    "\n",
    "pipe = Pipeline()\n",
    "sklearn_scaler = StandardScaler()\n",
    "sktime_scaler = TabularToSeriesAdaptor(sklearn_scaler)\n",
    "deseasonalizer = Deseasonalizer(sp=4)\n",
    "\n",
    "pipe = pipe.add_step(\n",
    "    sktime_scaler, name=\"scaler\", edges={\"X\": \"X__realgdp_realdpi_unemp\"}\n",
    ")\n",
    "pipe = pipe.add_step(\n",
    "    deseasonalizer, name=\"deseasonalizer\", edges={\"X\": \"X__realgdp_realdpi\"}\n",
    ")\n",
    "\n",
    "pipe = pipe.add_step(\n",
    "    MultiplexForecaster(\n",
    "        [\n",
    "            (\n",
    "                \"ridge\",\n",
    "                make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "            (\n",
    "                \"lasso\",\n",
    "                make_reduction(Lasso(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "        ]\n",
    "    ),\n",
    "    name=\"forecaster_gdp\",\n",
    "    edges={\"y\": \"deseasonalizer__realgdp\"},\n",
    ")\n",
    "\n",
    "pipe = pipe.add_step(\n",
    "    MultiplexForecaster(\n",
    "        [\n",
    "            (\n",
    "                \"ridge\",\n",
    "                make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "            (\n",
    "                \"lasso\",\n",
    "                make_reduction(Lasso(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "        ]\n",
    "    ),\n",
    "    name=\"forecaster_dpi\",\n",
    "    edges={\"y\": \"deseasonalizer__realdpi\"},\n",
    ")\n",
    "\n",
    "pipe = pipe.add_step(\n",
    "    MultiplexForecaster(\n",
    "        [\n",
    "            (\n",
    "                \"ridge\",\n",
    "                make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "            (\n",
    "                \"lasso\",\n",
    "                make_reduction(Lasso(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "        ]\n",
    "    ),\n",
    "    name=\"forecaster_unemp\",\n",
    "    edges={\n",
    "        \"y\": \"scaler__unemp\",\n",
    "        \"X\": [\n",
    "            \"forecaster_gdp\",\n",
    "            \"forecaster_dpi\",\n",
    "        ],\n",
    "    },\n",
    ")\n",
    "\n",
    "pipe = pipe.add_step(\n",
    "    MultiplexForecaster(\n",
    "        [\n",
    "            (\n",
    "                \"ridge\",\n",
    "                make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "            (\n",
    "                \"lasso\",\n",
    "                make_reduction(Lasso(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "        ]\n",
    "    ),\n",
    "    name=\"forecaster_inflation\",\n",
    "    edges={\"X\": [\"forecaster_dpi\", \"forecaster_unemp\"], \"y\": \"y\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "2. Specify the parameter grid:\n",
    "\n",
    "The keys of the dictionary are the parameters' in the pipeline, and the values specify which options should be tested.\n",
    "Keys have the following structure: parameter of a step `<step_name>__skobject__<parameter-name>` and input edges of a step `<step-name>__edges_<Xory>`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    \"forecaster_inflation__skobject__selected_forecaster\": [\"ridge\", \"lasso\"],\n",
    "    \"forecaster_unemp__skobject__selected_forecaster\": [\"ridge\", \"lasso\"],\n",
    "    \"forecaster_dpi__skobject__selected_forecaster\": [\"ridge\", \"lasso\"],\n",
    "    \"forecaster_gdp__skobject__selected_forecaster\": [\"ridge\", \"lasso\"],\n",
    "    \"forecaster_inflation__edges__X\": [\n",
    "        [\"forecaster_unemp\"],\n",
    "        [\"forecaster_unemp\", \"forecaster_dpi\"],\n",
    "    ],\n",
    "    \"forecaster_unemp__edges__X\": [\n",
    "        [],\n",
    "        [\"forecaster_dpi\"],\n",
    "        [\"forecaster_gdp\", \"forecaster_dpi\"],\n",
    "    ],\n",
    "    \"deseasonalizer__edges__X\": [\"X__realgdp_realdpi\", \"scaler__realgdp_realdpi\"],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Initialise the gridsearch using pipeline, cross-validation strategy, scoring, and param_grid.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sktime.forecasting.model_selection import (\n",
    "    ForecastingGridSearchCV,\n",
    "    SlidingWindowSplitter,\n",
    ")\n",
    "from sktime.performance_metrics.forecasting import mean_absolute_error\n",
    "\n",
    "gridcv = ForecastingGridSearchCV(\n",
    "    pipe,\n",
    "    cv=SlidingWindowSplitter(\n",
    "        window_length=len(X_train) - 20,\n",
    "        step_length=4,\n",
    "        fh=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
    "    ),\n",
    "    scoring=mean_absolute_error,\n",
    "    param_grid=param_grid,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Call fit on the gridsearch object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/benediktheidrich/code/sktime/sktime/forecasting/model_selection/_tune.py:201: UserWarning: in ForecastingGridSearchCV, n_jobs and pre_dispatch parameters are deprecated and will be removed in 0.27.0. Please use n_jobs and pre_dispatch directly in the backend_params argument instead.\n",
      "  warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  input_data[step_name] = pd.concat(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.997e+02, tolerance: 1.843e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.\n",
      "  warnings.warn(\n",
      "/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.997e+02, tolerance: 1.843e-01\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "                                                     10, 11, 12],\n",
       "                                                 step_length=4,\n",
       "                                                 window_length=171),\n",
       "                        forecaster=Pipeline(steps=[{&#x27;edges&#x27;: {&#x27;X&#x27;: &#x27;X__realgdp_realdpi_unemp&#x27;},\n",
       "                                                    &#x27;kwargs&#x27;: {},\n",
       "                                                    &#x27;method&#x27;: None,\n",
       "                                                    &#x27;name&#x27;: &#x27;scaler&#x27;,\n",
       "                                                    &#x27;skobject&#x27;: TabularToSeriesAdaptor(transformer=StandardScaler())},\n",
       "                                                   {&#x27;edges&#x27;: {&#x27;X&#x27;: &#x27;X__realgdp_realdpi&#x27;},\n",
       "                                                    &#x27;kwargs&#x27;: {},\n",
       "                                                    &#x27;method&#x27;: Non...\n",
       "                                    &#x27;forecaster_inflation__edges__X&#x27;: [[&#x27;forecaster_unemp&#x27;],\n",
       "                                                                       [&#x27;forecaster_unemp&#x27;,\n",
       "                                                                        &#x27;forecaster_dpi&#x27;]],\n",
       "                                    &#x27;forecaster_inflation__skobject__selected_forecaster&#x27;: [&#x27;ridge&#x27;,\n",
       "                                                                                            &#x27;lasso&#x27;],\n",
       "                                    &#x27;forecaster_unemp__edges__X&#x27;: [[],\n",
       "                                                                   [&#x27;forecaster_dpi&#x27;],\n",
       "                                                                   [&#x27;forecaster_gdp&#x27;,\n",
       "                                                                    &#x27;forecaster_dpi&#x27;]],\n",
       "                                    &#x27;forecaster_unemp__skobject__selected_forecaster&#x27;: [&#x27;ridge&#x27;,\n",
       "                                                                                        &#x27;lasso&#x27;]},\n",
       "                        scoring=&lt;function mean_absolute_error at 0x172c7e980&gt;)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class='sk-label-container'><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('402e7346-3509-42e3-a93d-8076f94ad6f3') type=\"checkbox\" ><label for=UUID('402e7346-3509-42e3-a93d-8076f94ad6f3') class='sk-toggleable__label sk-toggleable__label-arrow'>ForecastingGridSearchCV</label><div class=\"sk-toggleable__content\"><pre>ForecastingGridSearchCV(cv=SlidingWindowSplitter(fh=[1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                     10, 11, 12],\n",
       "                                                 step_length=4,\n",
       "                                                 window_length=171),\n",
       "                        forecaster=Pipeline(steps=[{&#x27;edges&#x27;: {&#x27;X&#x27;: &#x27;X__realgdp_realdpi_unemp&#x27;},\n",
       "                                                    &#x27;kwargs&#x27;: {},\n",
       "                                                    &#x27;method&#x27;: None,\n",
       "                                                    &#x27;name&#x27;: &#x27;scaler&#x27;,\n",
       "                                                    &#x27;skobject&#x27;: TabularToSeriesAdaptor(transformer=StandardScaler())},\n",
       "                                                   {&#x27;edges&#x27;: {&#x27;X&#x27;: &#x27;X__realgdp_realdpi&#x27;},\n",
       "                                                    &#x27;kwargs&#x27;: {},\n",
       "                                                    &#x27;method&#x27;: Non...\n",
       "                                    &#x27;forecaster_inflation__edges__X&#x27;: [[&#x27;forecaster_unemp&#x27;],\n",
       "                                                                       [&#x27;forecaster_unemp&#x27;,\n",
       "                                                                        &#x27;forecaster_dpi&#x27;]],\n",
       "                                    &#x27;forecaster_inflation__skobject__selected_forecaster&#x27;: [&#x27;ridge&#x27;,\n",
       "                                                                                            &#x27;lasso&#x27;],\n",
       "                                    &#x27;forecaster_unemp__edges__X&#x27;: [[],\n",
       "                                                                   [&#x27;forecaster_dpi&#x27;],\n",
       "                                                                   [&#x27;forecaster_gdp&#x27;,\n",
       "                                                                    &#x27;forecaster_dpi&#x27;]],\n",
       "                                    &#x27;forecaster_unemp__skobject__selected_forecaster&#x27;: [&#x27;ridge&#x27;,\n",
       "                                                                                        &#x27;lasso&#x27;]},\n",
       "                        scoring=&lt;function mean_absolute_error at 0x172c7e980&gt;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('3a6073ef-1a16-42c2-b892-0a284088ea45') type=\"checkbox\" ><label for=UUID('3a6073ef-1a16-42c2-b892-0a284088ea45') class='sk-toggleable__label sk-toggleable__label-arrow'>SlidingWindowSplitter</label><div class=\"sk-toggleable__content\"><pre>SlidingWindowSplitter(fh=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], step_length=4,\n",
       "                      window_length=171)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('57d6f38a-a78a-4e04-85e2-47eb86848af8') type=\"checkbox\" ><label for=UUID('57d6f38a-a78a-4e04-85e2-47eb86848af8') class='sk-toggleable__label sk-toggleable__label-arrow'>Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[{&#x27;edges&#x27;: {&#x27;X&#x27;: &#x27;X__realgdp_realdpi_unemp&#x27;}, &#x27;kwargs&#x27;: {},\n",
       "                 &#x27;method&#x27;: None, &#x27;name&#x27;: &#x27;scaler&#x27;,\n",
       "                 &#x27;skobject&#x27;: TabularToSeriesAdaptor(transformer=StandardScaler())},\n",
       "                {&#x27;edges&#x27;: {&#x27;X&#x27;: &#x27;X__realgdp_realdpi&#x27;}, &#x27;kwargs&#x27;: {},\n",
       "                 &#x27;method&#x27;: None, &#x27;name&#x27;: &#x27;deseasonalizer&#x27;,\n",
       "                 &#x27;skobject&#x27;: Deseasonalizer(sp=4)},\n",
       "                {&#x27;edges&#x27;: {&#x27;y&#x27;: &#x27;deseasonalizer__realgdp&#x27;}, &#x27;kwargs&#x27;: {},\n",
       "                 &#x27;method&#x27;: None...\n",
       "                                                                                                    window_length=5))])},\n",
       "                {&#x27;edges&#x27;: {&#x27;X&#x27;: [&#x27;forecaster_dpi&#x27;, &#x27;forecaster_unemp&#x27;],\n",
       "                           &#x27;y&#x27;: &#x27;y&#x27;},\n",
       "                 &#x27;kwargs&#x27;: {}, &#x27;method&#x27;: None, &#x27;name&#x27;: &#x27;forecaster_inflation&#x27;,\n",
       "                 &#x27;skobject&#x27;: MultiplexForecaster(forecasters=[(&#x27;ridge&#x27;,\n",
       "                                                               RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                    window_length=5)),\n",
       "                                                              (&#x27;lasso&#x27;,\n",
       "                                                               RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                    window_length=5))])}])</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ForecastingGridSearchCV(cv=SlidingWindowSplitter(fh=[1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                     10, 11, 12],\n",
       "                                                 step_length=4,\n",
       "                                                 window_length=171),\n",
       "                        forecaster=Pipeline(steps=[{'edges': {'X': 'X__realgdp_realdpi_unemp'},\n",
       "                                                    'kwargs': {},\n",
       "                                                    'method': None,\n",
       "                                                    'name': 'scaler',\n",
       "                                                    'skobject': TabularToSeriesAdaptor(transformer=StandardScaler())},\n",
       "                                                   {'edges': {'X': 'X__realgdp_realdpi'},\n",
       "                                                    'kwargs': {},\n",
       "                                                    'method': Non...\n",
       "                                    'forecaster_inflation__edges__X': [['forecaster_unemp'],\n",
       "                                                                       ['forecaster_unemp',\n",
       "                                                                        'forecaster_dpi']],\n",
       "                                    'forecaster_inflation__skobject__selected_forecaster': ['ridge',\n",
       "                                                                                            'lasso'],\n",
       "                                    'forecaster_unemp__edges__X': [[],\n",
       "                                                                   ['forecaster_dpi'],\n",
       "                                                                   ['forecaster_gdp',\n",
       "                                                                    'forecaster_dpi']],\n",
       "                                    'forecaster_unemp__skobject__selected_forecaster': ['ridge',\n",
       "                                                                                        'lasso']},\n",
       "                        scoring=<function mean_absolute_error at 0x172c7e980>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridcv.fit(y=y_train, X=X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine the results of the gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_test__DynamicForecastingErrorMetric</th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_pred_time</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test__DynamicForecastingErrorMetric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.539329</td>\n",
       "      <td>0.075673</td>\n",
       "      <td>0.023929</td>\n",
       "      <td>{'deseasonalizer__edges__X': 'X__realgdp_reald...</td>\n",
       "      <td>107.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.720565</td>\n",
       "      <td>0.076208</td>\n",
       "      <td>0.025338</td>\n",
       "      <td>{'deseasonalizer__edges__X': 'X__realgdp_reald...</td>\n",
       "      <td>119.5</td>\n",
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       "      <td>1.394329</td>\n",
       "      <td>0.141800</td>\n",
       "      <td>0.046452</td>\n",
       "      <td>{'deseasonalizer__edges__X': 'X__realgdp_reald...</td>\n",
       "      <td>97.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.942051</td>\n",
       "      <td>0.151181</td>\n",
       "      <td>0.045115</td>\n",
       "      <td>{'deseasonalizer__edges__X': 'X__realgdp_reald...</td>\n",
       "      <td>129.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.033714</td>\n",
       "      <td>0.160442</td>\n",
       "      <td>0.059711</td>\n",
       "      <td>{'deseasonalizer__edges__X': 'X__realgdp_reald...</td>\n",
       "      <td>136.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>187</th>\n",
       "      <td>1.329079</td>\n",
       "      <td>0.096761</td>\n",
       "      <td>0.037935</td>\n",
       "      <td>{'deseasonalizer__edges__X': 'scaler__realgdp_...</td>\n",
       "      <td>48.5</td>\n",
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       "    <tr>\n",
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       "      <td>1.329079</td>\n",
       "      <td>0.109997</td>\n",
       "      <td>0.040909</td>\n",
       "      <td>{'deseasonalizer__edges__X': 'scaler__realgdp_...</td>\n",
       "      <td>48.5</td>\n",
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       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>1.329079</td>\n",
       "      <td>0.100659</td>\n",
       "      <td>0.047549</td>\n",
       "      <td>{'deseasonalizer__edges__X': 'scaler__realgdp_...</td>\n",
       "      <td>48.5</td>\n",
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       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>1.329079</td>\n",
       "      <td>0.105045</td>\n",
       "      <td>0.055426</td>\n",
       "      <td>{'deseasonalizer__edges__X': 'scaler__realgdp_...</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>1.329079</td>\n",
       "      <td>0.106906</td>\n",
       "      <td>0.039190</td>\n",
       "      <td>{'deseasonalizer__edges__X': 'scaler__realgdp_...</td>\n",
       "      <td>48.5</td>\n",
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      ],
      "text/plain": [
       "     mean_test__DynamicForecastingErrorMetric  mean_fit_time  mean_pred_time  \\\n",
       "0                                    1.539329       0.075673        0.023929   \n",
       "1                                    1.720565       0.076208        0.025338   \n",
       "2                                    1.394329       0.141800        0.046452   \n",
       "3                                    1.942051       0.151181        0.045115   \n",
       "4                                    2.033714       0.160442        0.059711   \n",
       "..                                        ...            ...             ...   \n",
       "187                                  1.329079       0.096761        0.037935   \n",
       "188                                  1.329079       0.109997        0.040909   \n",
       "189                                  1.329079       0.100659        0.047549   \n",
       "190                                  1.329079       0.105045        0.055426   \n",
       "191                                  1.329079       0.106906        0.039190   \n",
       "\n",
       "                                                params  \\\n",
       "0    {'deseasonalizer__edges__X': 'X__realgdp_reald...   \n",
       "1    {'deseasonalizer__edges__X': 'X__realgdp_reald...   \n",
       "2    {'deseasonalizer__edges__X': 'X__realgdp_reald...   \n",
       "3    {'deseasonalizer__edges__X': 'X__realgdp_reald...   \n",
       "4    {'deseasonalizer__edges__X': 'X__realgdp_reald...   \n",
       "..                                                 ...   \n",
       "187  {'deseasonalizer__edges__X': 'scaler__realgdp_...   \n",
       "188  {'deseasonalizer__edges__X': 'scaler__realgdp_...   \n",
       "189  {'deseasonalizer__edges__X': 'scaler__realgdp_...   \n",
       "190  {'deseasonalizer__edges__X': 'scaler__realgdp_...   \n",
       "191  {'deseasonalizer__edges__X': 'scaler__realgdp_...   \n",
       "\n",
       "     rank_test__DynamicForecastingErrorMetric  \n",
       "0                                       107.5  \n",
       "1                                       119.5  \n",
       "2                                        97.5  \n",
       "3                                       129.5  \n",
       "4                                       136.0  \n",
       "..                                        ...  \n",
       "187                                      48.5  \n",
       "188                                      48.5  \n",
       "189                                      48.5  \n",
       "190                                      48.5  \n",
       "191                                      48.5  \n",
       "\n",
       "[192 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridcv.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the fitted grid search to make a prediction with the best hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>infl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006Q4</th>\n",
       "      <td>2.188182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q1</th>\n",
       "      <td>2.124281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q2</th>\n",
       "      <td>1.045280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q3</th>\n",
       "      <td>1.857716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q4</th>\n",
       "      <td>1.790664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q1</th>\n",
       "      <td>1.649457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q2</th>\n",
       "      <td>1.874361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q3</th>\n",
       "      <td>1.855627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q4</th>\n",
       "      <td>1.858207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009Q1</th>\n",
       "      <td>1.909693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009Q2</th>\n",
       "      <td>1.905106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009Q3</th>\n",
       "      <td>1.910452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            infl\n",
       "Period          \n",
       "2006Q4  2.188182\n",
       "2007Q1  2.124281\n",
       "2007Q2  1.045280\n",
       "2007Q3  1.857716\n",
       "2007Q4  1.790664\n",
       "2008Q1  1.649457\n",
       "2008Q2  1.874361\n",
       "2008Q3  1.855627\n",
       "2008Q4  1.858207\n",
       "2009Q1  1.909693\n",
       "2009Q2  1.905106\n",
       "2009Q3  1.910452"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = gridcv.predict(X=None, fh=y_test.index)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### How to implement a bit simpler version of the pipeline above by nesting sequential pipelines\n",
    "* Simplifcation: The forecasting of the unemployment rate is not dependent on the GDP and DPI.\n",
    "<img src=\"img/graphical_pipeline_simplified.png\" width=900 />\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Create sequential pipelines for forecasting the GDP, DPI and unemployment rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sktime.forecasting.compose import ColumnEnsembleForecaster, ForecastX\n",
    "from sktime.transformations.series.subset import ColumnSelect\n",
    "\n",
    "forecasting_pipeline_gdp = (\n",
    "    ColumnSelect([\"realgdp\"])  # To train the forecaster only on the realgdp column\n",
    "    * Deseasonalizer()\n",
    "    * MultiplexForecaster(\n",
    "        [\n",
    "            (\n",
    "                \"ridge\",\n",
    "                make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "            (\n",
    "                \"lasso\",\n",
    "                make_reduction(Lasso(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    ")\n",
    "forecasting_pipeline_dpi = (\n",
    "    ColumnSelect([\"realdpi\"])\n",
    "    * Deseasonalizer()\n",
    "    * MultiplexForecaster(\n",
    "        [\n",
    "            (\n",
    "                \"ridge\",\n",
    "                make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "            (\n",
    "                \"lasso\",\n",
    "                make_reduction(Lasso(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    ")\n",
    "\n",
    "forecasting_pipeline_unemp = (\n",
    "    ColumnSelect([\"unemp\"])\n",
    "    * Deseasonalizer()\n",
    "    * MultiplexForecaster(\n",
    "        [\n",
    "            (\n",
    "                \"ridge\",\n",
    "                make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "            (\n",
    "                \"lasso\",\n",
    "                make_reduction(Lasso(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Use ColumnEnsembleForecaster to combine the forecasts of the DPI, GDP, UNEMP. (Union of forecasts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_inflation_forecast = ColumnEnsembleForecaster(\n",
    "    [\n",
    "        (\"realdpi\", forecasting_pipeline_dpi, \"realdpi\"),\n",
    "        (\"realgdp\", forecasting_pipeline_gdp, \"realgdp\"),\n",
    "        (\"unemp\", forecasting_pipeline_unemp, \"unemp\"),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Create the inflation forecaster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inflation_forecast = ForecastX(\n",
    "    MultiplexForecaster(\n",
    "        [\n",
    "            (\n",
    "                \"ridge\",\n",
    "                make_reduction(Ridge(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "            (\n",
    "                \"lasso\",\n",
    "                make_reduction(Lasso(), windows_identical=False, window_length=5),\n",
    "            ),\n",
    "        ]\n",
    "    ),\n",
    "    input_inflation_forecast,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                                                              TransformedTargetForecaster(steps=[ColumnSelect(columns=[&#x27;realdpi&#x27;]),\n",
       "                                                                                                 Deseasonalizer(),\n",
       "                                                                                                 MultiplexForecaster(forecasters=[(&#x27;ridge&#x27;,\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                                                                                        window_length=5)),\n",
       "                                                                                                                                  (&#x27;lasso&#x27;,\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                                                                                        window_length...\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                                                                                        window_length=5)),\n",
       "                                                                                                                                  (&#x27;lasso&#x27;,\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                                                                                        window_length=5))])]),\n",
       "                                                              &#x27;unemp&#x27;)]),\n",
       "          forecaster_y=MultiplexForecaster(forecasters=[(&#x27;ridge&#x27;,\n",
       "                                                         RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                              window_length=5)),\n",
       "                                                        (&#x27;lasso&#x27;,\n",
       "                                                         RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                              window_length=5))]))</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class='sk-label-container'><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('fc73eb52-656e-424e-87ae-7dbe95cdecd4') type=\"checkbox\" ><label for=UUID('fc73eb52-656e-424e-87ae-7dbe95cdecd4') class='sk-toggleable__label sk-toggleable__label-arrow'>ForecastX</label><div class=\"sk-toggleable__content\"><pre>ForecastX(forecaster_X=ColumnEnsembleForecaster(forecasters=[(&#x27;realdpi&#x27;,\n",
       "                                                              TransformedTargetForecaster(steps=[ColumnSelect(columns=[&#x27;realdpi&#x27;]),\n",
       "                                                                                                 Deseasonalizer(),\n",
       "                                                                                                 MultiplexForecaster(forecasters=[(&#x27;ridge&#x27;,\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                                                                                        window_length=5)),\n",
       "                                                                                                                                  (&#x27;lasso&#x27;,\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                                                                                        window_length...\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                                                                                        window_length=5)),\n",
       "                                                                                                                                  (&#x27;lasso&#x27;,\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                                                                                        window_length=5))])]),\n",
       "                                                              &#x27;unemp&#x27;)]),\n",
       "          forecaster_y=MultiplexForecaster(forecasters=[(&#x27;ridge&#x27;,\n",
       "                                                         RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                              window_length=5)),\n",
       "                                                        (&#x27;lasso&#x27;,\n",
       "                                                         RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                              window_length=5))]))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('5f5206b1-8c1c-4a1e-b31e-70cd0f884f62') type=\"checkbox\" ><label for=UUID('5f5206b1-8c1c-4a1e-b31e-70cd0f884f62') class='sk-toggleable__label sk-toggleable__label-arrow'>ColumnSelect</label><div class=\"sk-toggleable__content\"><pre>ColumnSelect(columns=[&#x27;realdpi&#x27;])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('e079685a-900a-423e-9451-87cbb24cb1e8') type=\"checkbox\" ><label for=UUID('e079685a-900a-423e-9451-87cbb24cb1e8') class='sk-toggleable__label sk-toggleable__label-arrow'>Deseasonalizer</label><div class=\"sk-toggleable__content\"><pre>Deseasonalizer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('d278d2b4-3b81-42ba-bdaa-af5395cf11cd') type=\"checkbox\" ><label for=UUID('d278d2b4-3b81-42ba-bdaa-af5395cf11cd') class='sk-toggleable__label sk-toggleable__label-arrow'>Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('5eb8b5f3-4aea-4f4d-89e9-9005ac7d9ff2') type=\"checkbox\" ><label for=UUID('5eb8b5f3-4aea-4f4d-89e9-9005ac7d9ff2') class='sk-toggleable__label sk-toggleable__label-arrow'>Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('ffd5125b-1e70-4401-8d5e-3b74f979538e') type=\"checkbox\" ><label for=UUID('ffd5125b-1e70-4401-8d5e-3b74f979538e') class='sk-toggleable__label sk-toggleable__label-arrow'>ColumnSelect</label><div class=\"sk-toggleable__content\"><pre>ColumnSelect(columns=[&#x27;realgdp&#x27;])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('6ac26128-dbec-4a00-8183-22b5157c35ff') type=\"checkbox\" ><label for=UUID('6ac26128-dbec-4a00-8183-22b5157c35ff') class='sk-toggleable__label sk-toggleable__label-arrow'>Deseasonalizer</label><div class=\"sk-toggleable__content\"><pre>Deseasonalizer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('a86f7c0b-9894-4ead-88cc-ae7a5d7213cb') type=\"checkbox\" ><label for=UUID('a86f7c0b-9894-4ead-88cc-ae7a5d7213cb') class='sk-toggleable__label sk-toggleable__label-arrow'>Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('71f04df9-45cf-4a35-99cf-7a061d466c63') type=\"checkbox\" ><label for=UUID('71f04df9-45cf-4a35-99cf-7a061d466c63') class='sk-toggleable__label sk-toggleable__label-arrow'>Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('785d7c11-5178-41a3-be04-8ccf2e3644f7') type=\"checkbox\" ><label for=UUID('785d7c11-5178-41a3-be04-8ccf2e3644f7') class='sk-toggleable__label sk-toggleable__label-arrow'>ColumnSelect</label><div class=\"sk-toggleable__content\"><pre>ColumnSelect(columns=[&#x27;unemp&#x27;])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('4aed7b48-4222-466f-acba-f21e23393e06') type=\"checkbox\" ><label for=UUID('4aed7b48-4222-466f-acba-f21e23393e06') class='sk-toggleable__label sk-toggleable__label-arrow'>Deseasonalizer</label><div class=\"sk-toggleable__content\"><pre>Deseasonalizer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('8695733e-5b54-46b9-8ecd-83a2807bade5') type=\"checkbox\" ><label for=UUID('8695733e-5b54-46b9-8ecd-83a2807bade5') class='sk-toggleable__label sk-toggleable__label-arrow'>Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('83fb2230-a097-44c3-a198-fa1c44a2fa74') type=\"checkbox\" ><label for=UUID('83fb2230-a097-44c3-a198-fa1c44a2fa74') class='sk-toggleable__label sk-toggleable__label-arrow'>Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('bf173e0c-81dd-4158-a513-cdfc5ef0d48f') type=\"checkbox\" ><label for=UUID('bf173e0c-81dd-4158-a513-cdfc5ef0d48f') class='sk-toggleable__label sk-toggleable__label-arrow'>Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('46a92ed3-2e07-4345-9ba9-b67cd6563139') type=\"checkbox\" ><label for=UUID('46a92ed3-2e07-4345-9ba9-b67cd6563139') class='sk-toggleable__label sk-toggleable__label-arrow'>Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ForecastX(forecaster_X=ColumnEnsembleForecaster(forecasters=[('realdpi',\n",
       "                                                              TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),\n",
       "                                                                                                 Deseasonalizer(),\n",
       "                                                                                                 MultiplexForecaster(forecasters=[('ridge',\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                                                                                        window_length=5)),\n",
       "                                                                                                                                  ('lasso',\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                                                                                        window_length...\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                                                                                        window_length=5)),\n",
       "                                                                                                                                  ('lasso',\n",
       "                                                                                                                                   RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                                                                                        window_length=5))])]),\n",
       "                                                              'unemp')]),\n",
       "          forecaster_y=MultiplexForecaster(forecasters=[('ridge',\n",
       "                                                         RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                              window_length=5)),\n",
       "                                                        ('lasso',\n",
       "                                                         RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                              window_length=5))]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation_forecast.fit(y=y_train, X=X_train, fh=fh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>infl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006Q4</th>\n",
       "      <td>3.979318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q1</th>\n",
       "      <td>2.347512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q2</th>\n",
       "      <td>1.443598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q3</th>\n",
       "      <td>3.914533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007Q4</th>\n",
       "      <td>2.533117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q1</th>\n",
       "      <td>3.278010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q2</th>\n",
       "      <td>3.861517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q3</th>\n",
       "      <td>3.487510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008Q4</th>\n",
       "      <td>4.195074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009Q1</th>\n",
       "      <td>4.294984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009Q2</th>\n",
       "      <td>4.433578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009Q3</th>\n",
       "      <td>4.858610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            infl\n",
       "2006Q4  3.979318\n",
       "2007Q1  2.347512\n",
       "2007Q2  1.443598\n",
       "2007Q3  3.914533\n",
       "2007Q4  2.533117\n",
       "2008Q1  3.278010\n",
       "2008Q2  3.861517\n",
       "2008Q3  3.487510\n",
       "2008Q4  4.195074\n",
       "2009Q1  4.294984\n",
       "2009Q2  4.433578\n",
       "2009Q3  4.858610"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation_forecast.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'behaviour': 'update',\n",
       " 'columns': None,\n",
       " 'fh_X': None,\n",
       " 'fit_behaviour': 'use_actual',\n",
       " 'forecaster_X': ColumnEnsembleForecaster(forecasters=[('realdpi',\n",
       "                                        TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),\n",
       "                                                                           Deseasonalizer(),\n",
       "                                                                           MultiplexForecaster(forecasters=[('ridge',\n",
       "                                                                                                             RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                                                                  window_length=5)),\n",
       "                                                                                                            ('lasso',\n",
       "                                                                                                             RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                                                                  window_length=5))])]),\n",
       "                                        'realdpi'),\n",
       "                                       ('r...\n",
       "                                                                                                             RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                                                                  window_length=5))])]),\n",
       "                                        'realgdp'),\n",
       "                                       ('unemp',\n",
       "                                        TransformedTargetForecaster(steps=[ColumnSelect(columns=['unemp']),\n",
       "                                                                           Deseasonalizer(),\n",
       "                                                                           MultiplexForecaster(forecasters=[('ridge',\n",
       "                                                                                                             RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                                                                  window_length=5)),\n",
       "                                                                                                            ('lasso',\n",
       "                                                                                                             RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                                                                  window_length=5))])]),\n",
       "                                        'unemp')]),\n",
       " 'forecaster_y': MultiplexForecaster(forecasters=[('ridge',\n",
       "                                   RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                        window_length=5)),\n",
       "                                  ('lasso',\n",
       "                                   RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                        window_length=5))]),\n",
       " 'forecaster_X__forecasters': [('realdpi',\n",
       "   TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),\n",
       "                                      Deseasonalizer(),\n",
       "                                      MultiplexForecaster(forecasters=[('ridge',\n",
       "                                                                        RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                             window_length=5)),\n",
       "                                                                       ('lasso',\n",
       "                                                                        RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                             window_length=5))])]),\n",
       "   'realdpi'),\n",
       "  ('realgdp',\n",
       "   TransformedTargetForecaster(steps=[ColumnSelect(columns=['realgdp']),\n",
       "                                      Deseasonalizer(),\n",
       "                                      MultiplexForecaster(forecasters=[('ridge',\n",
       "                                                                        RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                             window_length=5)),\n",
       "                                                                       ('lasso',\n",
       "                                                                        RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                             window_length=5))])]),\n",
       "   'realgdp'),\n",
       "  ('unemp',\n",
       "   TransformedTargetForecaster(steps=[ColumnSelect(columns=['unemp']),\n",
       "                                      Deseasonalizer(),\n",
       "                                      MultiplexForecaster(forecasters=[('ridge',\n",
       "                                                                        RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                             window_length=5)),\n",
       "                                                                       ('lasso',\n",
       "                                                                        RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                             window_length=5))])]),\n",
       "   'unemp')],\n",
       " 'forecaster_X__realdpi': TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),\n",
       "                                    Deseasonalizer(),\n",
       "                                    MultiplexForecaster(forecasters=[('ridge',\n",
       "                                                                      RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                           window_length=5)),\n",
       "                                                                     ('lasso',\n",
       "                                                                      RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                           window_length=5))])]),\n",
       " 'forecaster_X__realgdp': TransformedTargetForecaster(steps=[ColumnSelect(columns=['realgdp']),\n",
       "                                    Deseasonalizer(),\n",
       "                                    MultiplexForecaster(forecasters=[('ridge',\n",
       "                                                                      RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                           window_length=5)),\n",
       "                                                                     ('lasso',\n",
       "                                                                      RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                           window_length=5))])]),\n",
       " 'forecaster_X__unemp': TransformedTargetForecaster(steps=[ColumnSelect(columns=['unemp']),\n",
       "                                    Deseasonalizer(),\n",
       "                                    MultiplexForecaster(forecasters=[('ridge',\n",
       "                                                                      RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                                                           window_length=5)),\n",
       "                                                                     ('lasso',\n",
       "                                                                      RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                                                           window_length=5))])]),\n",
       " 'forecaster_X__realdpi__steps': [ColumnSelect(columns=['realdpi']),\n",
       "  Deseasonalizer(),\n",
       "  MultiplexForecaster(forecasters=[('ridge',\n",
       "                                    RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                         window_length=5)),\n",
       "                                   ('lasso',\n",
       "                                    RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                         window_length=5))])],\n",
       " 'forecaster_X__realdpi__ColumnSelect': ColumnSelect(columns=['realdpi']),\n",
       " 'forecaster_X__realdpi__Deseasonalizer': Deseasonalizer(),\n",
       " 'forecaster_X__realdpi__MultiplexForecaster': MultiplexForecaster(forecasters=[('ridge',\n",
       "                                   RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                        window_length=5)),\n",
       "                                  ('lasso',\n",
       "                                   RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                        window_length=5))]),\n",
       " 'forecaster_X__realdpi__ColumnSelect__columns': ['realdpi'],\n",
       " 'forecaster_X__realdpi__ColumnSelect__index_treatment': 'remove',\n",
       " 'forecaster_X__realdpi__ColumnSelect__integer_treatment': 'col',\n",
       " 'forecaster_X__realdpi__Deseasonalizer__model': 'additive',\n",
       " 'forecaster_X__realdpi__Deseasonalizer__sp': 1,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__forecasters': [('ridge',\n",
       "   RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),\n",
       "  ('lasso',\n",
       "   RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5))],\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__selected_forecaster': None,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator': Ridge(),\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__pooling': 'local',\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__transformers': None,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__window_length': 5,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__alpha': 1.0,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__copy_X': True,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__fit_intercept': True,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__max_iter': None,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__positive': False,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__random_state': None,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__solver': 'auto',\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__tol': 0.0001,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator': Lasso(),\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__pooling': 'local',\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__transformers': None,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__window_length': 5,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__alpha': 1.0,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__copy_X': True,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__fit_intercept': True,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__max_iter': 1000,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__positive': False,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__precompute': False,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__random_state': None,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__selection': 'cyclic',\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__tol': 0.0001,\n",
       " 'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__warm_start': False,\n",
       " 'forecaster_X__realgdp__steps': [ColumnSelect(columns=['realgdp']),\n",
       "  Deseasonalizer(),\n",
       "  MultiplexForecaster(forecasters=[('ridge',\n",
       "                                    RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                         window_length=5)),\n",
       "                                   ('lasso',\n",
       "                                    RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                         window_length=5))])],\n",
       " 'forecaster_X__realgdp__ColumnSelect': ColumnSelect(columns=['realgdp']),\n",
       " 'forecaster_X__realgdp__Deseasonalizer': Deseasonalizer(),\n",
       " 'forecaster_X__realgdp__MultiplexForecaster': MultiplexForecaster(forecasters=[('ridge',\n",
       "                                   RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                        window_length=5)),\n",
       "                                  ('lasso',\n",
       "                                   RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                        window_length=5))]),\n",
       " 'forecaster_X__realgdp__ColumnSelect__columns': ['realgdp'],\n",
       " 'forecaster_X__realgdp__ColumnSelect__index_treatment': 'remove',\n",
       " 'forecaster_X__realgdp__ColumnSelect__integer_treatment': 'col',\n",
       " 'forecaster_X__realgdp__Deseasonalizer__model': 'additive',\n",
       " 'forecaster_X__realgdp__Deseasonalizer__sp': 1,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__forecasters': [('ridge',\n",
       "   RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),\n",
       "  ('lasso',\n",
       "   RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5))],\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__selected_forecaster': None,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator': Ridge(),\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__pooling': 'local',\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__transformers': None,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__window_length': 5,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__alpha': 1.0,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__copy_X': True,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__fit_intercept': True,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__max_iter': None,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__positive': False,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__random_state': None,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__solver': 'auto',\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__tol': 0.0001,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator': Lasso(),\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__pooling': 'local',\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__transformers': None,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__window_length': 5,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__alpha': 1.0,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__copy_X': True,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__fit_intercept': True,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__max_iter': 1000,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__positive': False,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__precompute': False,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__random_state': None,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__selection': 'cyclic',\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__tol': 0.0001,\n",
       " 'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__warm_start': False,\n",
       " 'forecaster_X__unemp__steps': [ColumnSelect(columns=['unemp']),\n",
       "  Deseasonalizer(),\n",
       "  MultiplexForecaster(forecasters=[('ridge',\n",
       "                                    RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                         window_length=5)),\n",
       "                                   ('lasso',\n",
       "                                    RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                         window_length=5))])],\n",
       " 'forecaster_X__unemp__ColumnSelect': ColumnSelect(columns=['unemp']),\n",
       " 'forecaster_X__unemp__Deseasonalizer': Deseasonalizer(),\n",
       " 'forecaster_X__unemp__MultiplexForecaster': MultiplexForecaster(forecasters=[('ridge',\n",
       "                                   RecursiveTabularRegressionForecaster(estimator=Ridge(),\n",
       "                                                                        window_length=5)),\n",
       "                                  ('lasso',\n",
       "                                   RecursiveTabularRegressionForecaster(estimator=Lasso(),\n",
       "                                                                        window_length=5))]),\n",
       " 'forecaster_X__unemp__ColumnSelect__columns': ['unemp'],\n",
       " 'forecaster_X__unemp__ColumnSelect__index_treatment': 'remove',\n",
       " 'forecaster_X__unemp__ColumnSelect__integer_treatment': 'col',\n",
       " 'forecaster_X__unemp__Deseasonalizer__model': 'additive',\n",
       " 'forecaster_X__unemp__Deseasonalizer__sp': 1,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__forecasters': [('ridge',\n",
       "   RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),\n",
       "  ('lasso',\n",
       "   RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5))],\n",
       " 'forecaster_X__unemp__MultiplexForecaster__selected_forecaster': None,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__estimator': Ridge(),\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__pooling': 'local',\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__transformers': None,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__window_length': 5,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__alpha': 1.0,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__copy_X': True,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__fit_intercept': True,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__max_iter': None,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__positive': False,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__random_state': None,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__solver': 'auto',\n",
       " 'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__tol': 0.0001,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator': Lasso(),\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__pooling': 'local',\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__transformers': None,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__window_length': 5,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__alpha': 1.0,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__copy_X': True,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__fit_intercept': True,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__max_iter': 1000,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__positive': False,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__precompute': False,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__random_state': None,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__selection': 'cyclic',\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__tol': 0.0001,\n",
       " 'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__warm_start': False,\n",
       " 'forecaster_y__forecasters': [('ridge',\n",
       "   RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),\n",
       "  ('lasso',\n",
       "   RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5))],\n",
       " 'forecaster_y__selected_forecaster': None,\n",
       " 'forecaster_y__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),\n",
       " 'forecaster_y__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),\n",
       " 'forecaster_y__ridge__estimator': Ridge(),\n",
       " 'forecaster_y__ridge__pooling': 'local',\n",
       " 'forecaster_y__ridge__transformers': None,\n",
       " 'forecaster_y__ridge__window_length': 5,\n",
       " 'forecaster_y__ridge__estimator__alpha': 1.0,\n",
       " 'forecaster_y__ridge__estimator__copy_X': True,\n",
       " 'forecaster_y__ridge__estimator__fit_intercept': True,\n",
       " 'forecaster_y__ridge__estimator__max_iter': None,\n",
       " 'forecaster_y__ridge__estimator__positive': False,\n",
       " 'forecaster_y__ridge__estimator__random_state': None,\n",
       " 'forecaster_y__ridge__estimator__solver': 'auto',\n",
       " 'forecaster_y__ridge__estimator__tol': 0.0001,\n",
       " 'forecaster_y__lasso__estimator': Lasso(),\n",
       " 'forecaster_y__lasso__pooling': 'local',\n",
       " 'forecaster_y__lasso__transformers': None,\n",
       " 'forecaster_y__lasso__window_length': 5,\n",
       " 'forecaster_y__lasso__estimator__alpha': 1.0,\n",
       " 'forecaster_y__lasso__estimator__copy_X': True,\n",
       " 'forecaster_y__lasso__estimator__fit_intercept': True,\n",
       " 'forecaster_y__lasso__estimator__max_iter': 1000,\n",
       " 'forecaster_y__lasso__estimator__positive': False,\n",
       " 'forecaster_y__lasso__estimator__precompute': False,\n",
       " 'forecaster_y__lasso__estimator__random_state': None,\n",
       " 'forecaster_y__lasso__estimator__selection': 'cyclic',\n",
       " 'forecaster_y__lasso__estimator__tol': 0.0001,\n",
       " 'forecaster_y__lasso__estimator__warm_start': False}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation_forecast.get_params(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Comparison graphical pipeline with nesting of sequential pipelines\n",
    "\n",
    "### Advantages of graphical pipelines\n",
    "* Enable an easy implementation of complex pipelines\n",
    "    * By nesting sequential pipelines, even a simplified version of the graphical pipeline is very complicat to implement.\n",
    "    * By nesting sequential pipelines, some graphical pipelines are not possible to implement (e.g., the example with coupled ForecastX).\n",
    "* Preprocessing steps can not be shared between the different forecasters.\n",
    "* The parameter structure can be very complex for the sequential pipelines.\n",
    "* In a complex scenario, how would you fine-tune the edges?\n",
    "\n",
    "### Advantages of sequential pipelines\n",
    "* Constructing simple pipelines is very easy.\n",
    "* Inverse operations are automatically applied.\n",
    "* This is a mature feature compared to the experimental graphical pipeline.\n",
    "\n",
    "\n",
    "### When to use what? \n",
    "* If your pipeline does not need much of nested pipelines and is mainly sequential, you should probably stick with the standard pipeline implementation.\n",
    "* If your pipeline should represent a complex scenario with multiple forecasters, that are influencing other ones, you might want to use the graphical pipeline since it makes it easier to write the codel\n"
   ]
  }
 ],
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